Recently, generic object recognition by which the name of an object in an image is automatically recognized has been actively studied. For example, by the generic object recognition, efficient image search may be performed as long as an object name is automatically assigned to an image as meta-data.
(1) Creation of an Image Corpus
A basic procedure of automatically assigning meta-data by the generic object recognition is described below.
In the generic object recognition, a large number of images to which a correct object name is assigned beforehand are prepared for each object name. Hereinafter, an image with a correct object name is referred to as an image corpus (training image).
(2) Machine Learning
Relationships between images and correct object names may be automatically obtained by machine learning.
(3) Automatic Assignment of Meta-Data
Meta-data is automatically assigned to an unknown image using the relationship that is obtained by the machine learning of (2).
In the above-described procedure, correct meta-data is assigned to an image manually in order to create the image corpus of (1), so that it takes enormous effort. The meta-data is, for example, an object name. Therefore, it is desirable that the manual assigning operation is streamlined.
Therefore, a case is described below in which correct meta-data is assigned manually. For example, it is assumed that, for a satellite or aerial photo that is displayed on a screen, an area of a pylon is specified manually by a person who performs input, and a “pylon” is input to the field of an object name. In the manual assigning operation, the following two procedures are performed.
(A) To specify an area of an object by a person who performs input.
(B) To input a correct name of the specified object by the person who performs input.
In the end, when the person who performs input gives saving instruction, a pair of a partial image of the specified area and the correct object name is saved as an image corpus.
On the other hand, as a known technology by which meta-data is assigned, for example, there is a technology for associating, by a management server, meta-data of material contents that are created by an editing terminal with corresponding accuracy, and storing the associated meta-data and accuracy in a database (DB).
Image corpus construction operation may be streamlined by performing similar area search. For example, there is a technology for automatically searching for an area the appearance (image feature amount) of which is similar to an area specified manually. As the image feature amount, a feature amount of color, shape, and the like are employed.
As a technology by which similar area search is streamlined, there is a technology of moving a reference image, calculating a matching degree of a captured image and a reference image when matching of the two images is performed, detecting a certain primary matching candidate point, and performing secondary matching in descending order of matching degree.
Japanese Laid-open Patent Publication No. 2009-260693 and Japanese Laid-open Patent Publication No. 6-168331 are examples of the related art.
Susumu Endo, Shuichi Shiitani, Yusuke Uehara, Daiki Masumoto, and Shigemi Nagata: MIRACLES: Multimedia Information Retrieval, Classification, and Exploration System, ICME2002, proceedings of ICME2002 (2002) is also an example of the related art.